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faster-ssd.py
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faster-ssd.py
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#encoding: utf-8
from __future__ import print_function, division
# 第三方
import os
import sys
import pyglet
import time
import tensorflow as tf
import random
import numpy as np
from collections import deque
from config import Param
from hyperparameter import *
from loss import *
from Specific_net.inceptionv3_facenet import *
from layers.rpn_anchor_target import *
import datetime
class framework():
def __init__(self,env,optimizeMethod='adam'):
self.optimizeMethod = optimizeMethod
self.Work_Param=Param("ZWW_face_detection_config_112inception_out8.json")
self.train_batch=32
self.val_batch=32
self.test_batch=1
self.input_image_size=[3,self.Work_Param.max_size,self.Work_Param.max_size]
self.rpn_variable=[]
self.rcnn_variable=[]
# Graph Related
self.graph = tf.Graph()
# 初始化
self.train=self.faster_ssd()
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
self.sess = tf.Session(graph=self.graph,config=config)
'''
if TRAIN:
self.writer = tf.summary.FileWriter('Graphview/', self.graph)
# 放在定义Graph之后，保存这张计算图
self.saver = tf.train.Saver(self.all_weights)
'''
def faster_ssd(self):
'''
定义faster-ssd的计算图谱
'''
with self.graph.as_default():
# Training computation.
self.small_rpn_train=InceptionV3_facenet_112(batch_seize=train_batch,num_channels=self.input_image_size[0],image_size_h=self.input_image_size[1],image_size_w=self.input_image_size[2],
trainable=True,resueable=False,BN_decay=0.99,BN_epsilon=0.00001,anchor_num=15,name="train_rpn/")
self.rpn_target_layer=anchor_target_layer(self.small_rpn_train.roi_data,batch=train_batch,img_height=self.input_image_size[1],img_width=self.input_image_size[2],FrcnnParam=self.Work_Param)
'''
self.small_rpn_val=InceptionV3_facenet_112(batch_seize=val_batch,num_channels=self.input_image_size[0],image_size_h=self.input_image_size[1],image_size_w=self.input_image_size[2],
trainable=False,resueable=True,BN_decay=0.99,BN_epsilon=0.00001,anchor_num=15,name="val/")
self.small_rpn_test=InceptionV3_facenet_112(batch_seize=test_batch,num_channels=self.input_image_size[0],image_size_h=self.input_image_size[1],image_size_w=self.input_image_size[2],
trainable=False,resueable=True,BN_decay=0.99,BN_epsilon=0.00001,anchor_num=15,name="test/")
'''
#self.merged_train_weights_summary= tf.summary.merge(summaries)
#self.summary_placeholders, self.update_ops, self.summary_op = self.setup_summary()
for var in tf.trainable_variables():
if var.op.name.find('train_rpn') > 0:
self.rpn_variable.append(var)
train=loss(self.small_rpn_train.rpn_cls_score_reshape,self.small_rpn_train.rpn_bbox_pred,self.rpn_variable)
return train
def loss(self,rpn_cls,rpn_reg,rpn_params,Regularization='NULL'):
train=loss_fun(self,rpn_cls,rpn_reg,rpn_params,Regularization='NULL')
return train